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Tailoring atomic 1T phase CrTe2 for in situ fabrication

机译:定制原子 1T 相 CrTe2 用于原位制造

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摘要

Controllable tailoring and understanding the phase-structure relationship of the 1T phase two-dimensional (2D) materials are critical for their applications in nanodevices. The in situ transmission electron microscope (TEM) could regulate and monitor the evolution process of the nanostructure of 2D material with atomic resolution. In this work, a controllably tailoring 1T-CrTe2 nanopore is carried out by the in situ TEM. A preferred formation of the 1T-CrTe2 border structure and nanopore healing process are studied at the atomic scale. The controllable tailoring of the 1T phase nanopore could be achieved by regulating the transformation of two types of low indices of crystal faces {10 (1) over bar0} and {11 (2) over bar0} at the nanopore border. Machine learning is applied to automatically process the TEM images with high efficiency. By adopting the deep-learning-based image segmentation method and augmenting the TEM images specifically, the nanopore of the TEM image could be automatically identified and the evaluation result of DICE metric reaches 93.17 on test set. This work presents the unique structure evolution of 1T phase 2D material and the computer aided high efficiency TEM data analysis based on deep learning. The techniques applied in this work could be generalized to other materials for controlled nanostructure regulation and automatic TEM image analyzation.
机译:可控的剪裁和理解 1T 相二维 (2D) 材料的相-结构关系对于其在纳米器件中的应用至关重要。原位透射电子显微镜(TEM)可以以原子分辨率调控和监测二维材料纳米结构的演化过程。在这项工作中,通过原位透射电镜进行了可控的定制1T-CrTe2纳米孔。在原子尺度上研究了1T-CrTe2边界结构的优选形成和纳米孔愈合过程.1T相纳米孔的可控剪裁可以通过调控纳米孔边界{10 (1) over bar0}和{11 (2) over bar0}两种低折射率晶面的转变来实现。应用机器学习来高效地自动处理TEM图像。采用基于深度学习的图像分割方法,并对TEM图像进行具体增强,可以自动识别TEM图像的纳米孔,在测试集上DICE度量的评价结果达到93.17%。本文介绍了1T相二维材料的独特结构演化和基于深度学习的计算机辅助高效透射电镜数据分析。本工作所应用的技术可以推广到其他材料中,用于受控纳米结构调控和自动TEM图像分析。

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